Intelligent Point-of-Interest Recommendation for Tourism Planning via Density-based Clustering and Genetic Algorithm

In recent years, geographic information service and relevant social media become more popular, some geographic point may interest people, e.g. scenic spot or famous store, naming as a point-of-interest (POI). However, the number of POI contributing by social media grows exponentially which causing a searching problem. How to recommend a POI to a user/tourist becomes a challenge. This study proposes an intelligent system using density-based clustering and genetic algorithm to recommend a POIs solution for tourism planning. Density-based clustering identifies candidate POIs. Skyline method decides a superior POI from candidate POIs by dominant of multiple attributes. Genetic algorithm optimizes the recommendation solution. The contribution is to get a tourism POI solution from a huge amount of candidate POIs based on user/tourist preferences. An experimental system implementation is in progress. In future, we will use open data from Google map and Foursquare to proof the proposed system mechanism effectiveness

Playing Cassino with Reinforcement Learning

Reinforcement learning algorithms have been used to create game-playing agents for various games—mostly, deterministic games such as chess, shogi, and Go. This study used Deep-Q reinforcement learning to create an agent that plays a non-deterministic card game, Cassino. This agent’s performance was compared against the performance of a Cassino mobile app. Results showed that the trained models did not perform well and had trouble training around build actions which are important in Cassino. Future research could experiment with other reinforcement learning algorithms to see if they are better at training around build actions

Speech to text conversion and summarization for effective understanding and documentation

Speech, is the most powerful way of communication with which human beings express their thoughts and feelings through different languages. The features of speech differs with each language. However, even while communicating in the same language, the pace and the dialect varies with each person. This creates difficulty in understanding the conveyed message for some people. Sometimes lengthy speeches are also quite difficult to follow due to reasons such as different pronunciation, pace and so on.   Speech recognition which is an inter disciplinary field of computational linguistics aids in developing technologies that empowers the recognition and translation of speech into text. Text summarization extracts the utmost important information from a source which is a text and provides the adequate summary of the same. The research work presented in this paper describes an easy and effective method for speech recognition. The speech is converted to the corresponding text and produces summarized text. This has various applications like lecture notes creation, summarizing catalogues for lengthy documents and so on. Extensive experimentation is performed to validate the efficiency of the proposed metho

Investigating spatio-temporal mobility patterns and changes in metro usage under the impact of COVID-19 using Taipei Metro smart card data

Modern public transit systems are often run with automated fare collection (AFC) systems in combination with smart cards. These systems passively collect massive amounts of detailed spatio-temporal trip data, thus opening up new possibilities for public transit planning and management as well as providing new insights for urban planners. We use smart card trip data from Taipei, Taiwan, to perform an in-depth analysis of spatio-temporal station-to-station metro trip patterns for a whole week divided into several time slices. Based on simple linear regression and line graphs, days of the week and times of the day with similar temporal passenger flow patterns are identified. We visualize magnitudes of passenger flow based on actual geography. By comparing flows for January to March 2019 and for January to March 2020, we look at changes in metro trips under the impact of the coronavirus pandemic (COVID-19) that caused a state of emergency around the globe in 2020. Our results show that metro usage under the impact of COVID-19 has not declined uniformly, but instead is both spatially and temporally highly heterogeneous

Global Deluge, Theophany and the Ut-napištim-Noah-Oppehnaboon Connection

It is a long established fact that stories of a global flood permeate oral traditions and mythologies in every corner of the Earth. Of these global deluge epics, the most well-known are those of the biblical Noah and of Ut-napištim recorded in the Sumerian Epic of Gilgamesh, both of which were recorded in antiquity. As such, any comparisons of flood texts can reasonably begin with a consideration of the similarities and differences of a flood myth with those of Noah and of Ut-napištim, and they often are.Taiwan’s Saisiyat tribal myth of Oppehnaboon is remarkably similar to the accounts of both Ut-napištim and Noah. The current study examines correlations in character background, communication with and manifestation of transcendental messengers (theophany), stated causes for the deluge, post-flood commandments and other parallels which are featured in the accounts. This study sheds light on one of the lesser known Saisiyat tribe’s myths of Oppehnaboon and serves as a first step to a more in depth investigation of Formosan global deluge myths. Keywords: Formosan mythography; Saisiyat; Theophany; Flood myth; Oppehnaboon; Ut-napištim; Noah DOI: 10.7176/JLLL/83-01 Publication date: November 30th 202

AirKit: A Citizen-Sensing Toolkit for Monitoring Air Quality.

Increasing urbanisation and a better understanding of the negative health effects of air pollution have accelerated the use of Internet of Things (IoT)-based air quality sensors. Low-cost and low-power sensors are now readily available and commonly deployed by individuals and community groups. However, there are a wide range of such IoT devices in circulation that differently focus on problems of sensor validation, data reliability, or accessibility. In this paper, we present AirKit, which was developed as an integrated and open source "social IoT technology". AirKit enables a comprehensive approach to citizen-sensing air quality through several integrated components: (1) the Dustbox 2.0, a particulate matter sensor; (2) Airsift, a data analysis platform; (3) a reliable and automatic remote firmware update system; (4) a "Data Stories" method and tool for communicating citizen data; and (5) an AirKit logbook that provides a guide for designing and running air quality projects, along with instructions for building and using AirKit components. Developed as a social technology toolkit to foster open processes of research co-creation and environmental action, Airkit has the potential to generate expanded engagements with IoT and air quality by improving the accuracy, legibility and use of sensors, data analysis and data communication.This research was supported by the European Research Council under the European Union’s Seventh Framework Programme (FP/2007–2013)/ERC Grant Agreement n. 313347, “Citizen Sensing and Environmental Practice: Assessing Participatory Engagements with Environments through Sensor Technologies”, and from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (ERC Grant Agreement n. 779921), “AirKit: Citizen Sense Air Monitoring Kit”. The University of Cambridge provided additional support through the ESRC Impact Acceleration Account (2020) for enabling impact

Multimodal Automated Fact-Checking: A Survey

Misinformation is often conveyed in multiple modalities, e.g. a miscaptioned image. Multimodal misinformation is perceived as more credible by humans, and spreads faster than its text-only counterparts. While an increasing body of research investigates automated fact-checking (AFC), previous surveys mostly focus on text. In this survey, we conceptualise a framework for AFC including subtasks unique to multimodal misinformation. Furthermore, we discuss related terms used in different communities and map them to our framework. We focus on four modalities prevalent in real-world fact-checking: text, image, audio, and video. We survey benchmarks and models, and discuss limitations and promising directions for future researchComment: The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP): Finding

Machine Learning in Dentistry: A Scoping Review

Machine learning (ML) is being increasingly employed in dental research and application. We aimed to systematically compile studies using ML in dentistry and assess their methodological quality, including the risk of bias and reporting standards. We evaluated studies employing ML in dentistry published from 1 January 2015 to 31 May 2021 on MEDLINE, IEEE Xplore, and arXiv. We assessed publication trends and the distribution of ML tasks (classification, object detection, semantic segmentation, instance segmentation, and generation) in different clinical fields. We appraised the risk of bias and adherence to reporting standards, using the QUADAS-2 and TRIPOD checklists, respectively. Out of 183 identified studies, 168 were included, focusing on various ML tasks and employing a broad range of ML models, input data, data sources, strategies to generate reference tests, and performance metrics. Classification tasks were most common. Forty-two different metrics were used to evaluate model performances, with accuracy, sensitivity, precision, and intersection-over-union being the most common. We observed considerable risk of bias and moderate adherence to reporting standards which hampers replication of results. A minimum (core) set of outcome and outcome metrics is necessary to facilitate comparisons across studies